design-data-extraction
Define a structured extraction form, field semantics, coding rules, and missing-data handling for evidence synthesis.
Skill by yogsoth-ai
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: design, data
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/design-data-extraction
What's inside
- Skill name
design-data-extraction- Description
- Define a structured extraction form, field semantics, coding rules, and missing-data handling for evidence synthesis.
- License
- Apache-2.0
- Instructions
- 44 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# design-data-extraction ## Purpose Define a structured extraction form, field semantics, coding rules, and missing-data handling for evidence synthesis. ## Input contract ```yaml required: [evidence_question, record_schema] optional: [coding_guidance, unit_rules, quality_fields] constraints: [field definitions and missing-data states must be explicit] ``` ## Procedure 1. Derive fields from the evidence question and synthesis outputs. 2. Define types, units, allowed values, coding rules, and provenance fields. 3. Specify unknown, not reported, not applicable, and ambiguous states. 4. Pilot the form on boundary records and revise only documented ambiguities. If the extraction schema is fixed and studies are ready for methodological appraisal, consider `audit-study-validity` as the next tactic. ## Output contract ```yaml produces: [extraction_schema, coding_rules, missing_data_policy, pilot_issues] delta_fields: [decisions, assumption_updates, uncertainties, open_questions] ``` ## Quality gates - Every output field has an extraction source and semantic definition. - Missingness is not conflated with a negative finding. ## Failure and counterexamples Do not add fields that cannot affect the synthesis, and do not collapse incomparable units. ## Provenance map - `resolved: data-extraction-form`
From yogsoth-ai/de-anthropocentric-research-engine, Apache-2.0.
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Signals
- Stars
- 505
- Forks
- 43
- Open issues
- 1
- Last commit (this folder)
- Sep 23, 2026 (1 month ago)
- Last release
- Oct 23, 2026 (8 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Convert a sharp scientific claim into the cheapest decisive falsification program.
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adjust-abstraction-scope
yogsoth-ai
Move a scientific object up/down in abstraction or narrow/broaden selected scope dimensions (population, mechanism, context, outcome, timeframe, system…
SkillAFreshness: A★ 505Claude Code +9 - 0.600score
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Apply the pre-declared statistical/estimation procedure and report effect size, uncertainty, model checks, and decision-relevant interpretation.
SkillAFreshness: A★ 505Claude Code +9 - 0.600score
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Estimate an effective number of independent evidence/reasoning paths after discounting dependence/correlation; report nominal N, dependence structure, and…
SkillAFreshness: A★ 505Claude Code +9 - 0.600score
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design-data-extraction skill. Agent Mart. https://agentmart.dev/skills/yogsoth-ai-design-data-extraction (last updated 2026-10-24)